If your organization is considering implementing knowledge graphs (KGs) or large language models (LLMs) at the enterprise level, TopQuadrant’s Steve Hedden’s latest post in Towards Data Science provides a practical overview of the currently available methods.

Here is a quick summary

LLMs are enabling faster KG creation and curation through:

• Entity resolution
• Automated tagging of unstructured data
• Entity and class extraction

At the same time, KGs are being used in LLM and GenAI pipelines for:

• Data governance, access control, and regulatory compliance
• Accuracy and contextual understanding via RAG, prompt-to-query, or fine-tuning
• Efficiency and scalability

In this post, Steve discusses each of these in more detail, the pros and cons of the different approaches, and where different companies like TopQuadrant fit.

Click Here to read the full blog